Table of Contents
The wall between SAP and generative AI
SAP systems hold enormous amounts of business data. Combining them with generative AI promises a step-change in productivity, yet many companies hit a wall: confidential data cannot be sent to external cloud AI services.
Why local LLMs?
The biggest benefit is that data never leaves your environment. You keep full control over logs, costs are predictable, and offline or restricted environments are supported.
Practical use cases
- Knowledge search and Q&A: retrieve answers from manuals and incident procedures using RAG
- Incident support: match error messages with past incidents to suggest causes and next steps
- Code generation and review: ABAP skeletons and code explanation
- Report summarization: draft summaries of monthly figures for management
A 4-step adoption roadmap
- Requirements — clarify processes, users, accuracy, and budget
- Technical validation — compare candidate models
- PoC — validate with real data
- Production — deploy with operations, monitoring, and retraining
Points to design in from day one
- Mitigations for hallucinated answers and grounded responses
- Balance between model size and hardware cost
- Access control and audit logging for SAP data
Summary
The value of SAP × AI is using accumulated business data and field knowledge safely, quickly, and effectively. Local LLMs are a realistic way to get there. If you are combining AI adoption with a core-system migration, see S/4HANA Migration: 5 Points to Get Right.